Big data analytics is an emerging technique for efficiently processing huge amount of data for different applications. One of a useful application of the big data analytics is deriving business rules for enterprises. The business rules implemented by the enterprises are not only used for taking smoother and efficient decisions, but it also helps to understand the tradeoffs. Traditionally, the business rules are derived based on predefined criteria, knowledge of compliances, market conditions, and other numerous boundaries of data associated with a particular line-of-business.
However, this traditional way for deriving the business rule does not compete with dynamic business requirements. This is because, every day scenarios and requirements are dynamically changing. It is therefore required to configure the business rules based on current situation and ongoing trend in the particular line-of-business. Off-the-shelf there exist various tools that facilitates the configuration of business rules, however, they have no capabilities to change them in real-time and include context of scenarios.
Thus, considering the context of the scenarios is one of a challenge while deriving the business rules. Apart from this issue, orchestrating the business rules is another major challenge. Currently, developers of business rule orchestration receive requirements from the business users to design the orchestration, and therefore, a manual intervention is required. Thus, there is a gap in automating the dynamic selection of business rules and its orchestration.